Can my AI detect if a silobag has been ruptured or damaged?

Yes! Use the `get_rupture_alerts` tool to check for satellite-detected silobag ruptures, tears, or structural damage. Wiagro uses satellite imagery analysis to identify breaches in silobag integrity that could expose grain to weather and pests. For a complete picture, combine with `get_alerts` to see temperature, humidity, and CO2 alerts that may indicate secondary effects of a rupture.

How do I monitor CO2 levels to detect early grain spoilage in silobags?

Use the `get_co2_history` tool with your silobag ID and a date range (e.g., 30 days) to see CO2 trends over time. Rising CO2 levels indicate biological activity from mold, insects, or grain respiration — often appearing before temperature changes. Combine with `get_current_readings` for real-time CO2 status and `get_alerts` to check for any active CO2 warnings.

Can I check the health status of sensors in my silobag monitoring system?

Yes! Use the `get_sensor_health` tool with your silobag ID to check battery levels, signal strength, and operational status of all IoT sensors. This helps you identify sensors that need battery replacement or have gone offline, ensuring continuous monitoring coverage. For a facility-wide view, use `get_facility_overview` to see the overall health of your monitoring system.

Wiagro MCP Server for LangChain 12 tools — connect in under 2 minutes

Name: Wiagro
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Wiagro through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "wiagro": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Wiagro, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Wiagro MCP Server

Connect your Wiagro Smart Silobag API to any AI agent and take full control of IoT-based grain condition monitoring, rupture detection, satellite environmental monitoring, and silobag quality management through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Wiagro through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Silobag Management — List and manage all silobags and conventional silos with grain types, fill levels, and monitoring status
Real-Time Readings — Get current temperature, intergranular humidity, and CO2 readings from IoT sensors throughout the grain mass
Temperature History — Track historical temperature trends to detect hot spots and spoilage heating
Humidity History — Monitor intergranular humidity patterns for moisture migration and condensation detection
CO2 Tracking — Follow CO2 trends as the earliest indicator of biological activity and grain spoilage
Rupture Detection — Receive satellite-based alerts for silobag tears, holes, and structural damage
Alert Management — Monitor active alerts for high temperature, humidity, and CO2 threshold breaches
Sensor Health — Track IoT sensor battery levels, signal strength, and operational status
Satellite Monitoring — Access satellite-based environmental data affecting silobag conditions
Quality Assessment — Get AI-powered grain quality scores with storage life predictions
Facility Overview — Get comprehensive facility-wide summaries for executive reporting

The Wiagro MCP Server exposes 12 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Wiagro to LangChain via MCP

Follow these steps to integrate the Wiagro MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 12 tools from Wiagro via MCP

Why Use LangChain with the Wiagro MCP Server

LangChain provides unique advantages when paired with Wiagro through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Wiagro MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Wiagro queries for multi-turn workflows

Wiagro + LangChain Use Cases

Practical scenarios where LangChain combined with the Wiagro MCP Server delivers measurable value.

RAG with live data: combine Wiagro tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Wiagro, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Wiagro tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Wiagro tool call, measure latency, and optimize your agent's performance

Wiagro MCP Tools for LangChain (12)

These 12 tools become available when you connect Wiagro to LangChain via MCP:

get_alerts

Returns alert severity (critical, warning, info), alert type, affected silobag, timestamp, and recommended actions. Essential for proactive grain management, quality issue detection, and operational response. AI agents should use this when users ask "show me all active alerts", "what warnings have been triggered for silobag 3", or need alert data for operational monitoring. Optional silobag_id filters alerts for a specific silobag. Get active temperature, humidity, and CO2 alerts for silobags

get_co2_history

CO2 is the earliest indicator of biological activity (mold, insects, grain respiration) that leads to spoilage. Returns time-series CO2 data in ppm with timestamps. Essential for spoilage trend analysis, early warning detection, and validating storage condition stability. AI agents should reference this when users ask "show me CO2 trends for silobag 3 over the past 30 days", "has CO2 been rising in silobag 5", or need historical CO2 data for grain quality assessment. Get historical CO2 readings to track biological activity and spoilage trends

get_current_readings

Returns temperature (Celsius), intergranular humidity (%), and CO2 levels (ppm) from multiple sensor positions throughout the grain mass. Essential for real-time grain quality monitoring, early spoilage detection, and storage condition assessment. AI agents should use this when users ask "what are the current conditions in silobag 2", "show me all sensor readings for silobag 4", or need immediate grain quality data for storage management decisions. Get current temperature, humidity, and CO2 readings from sensors in a silobag

get_facility_overview

Essential for executive reporting, facility-wide quality assessment, and strategic storage management. AI agents should use this when users ask "give me an overview of my entire facility", "what is the overall grain quality status", or need facility-level summaries for management reporting. Get comprehensive overview of all monitored silobags and storage units

get_humidity_history

Humidity migration and condensation are key drivers of spoilage and quality loss. Returns time-series humidity data (%) with timestamps from multiple sensor positions. Essential for moisture migration analysis, condensation detection, and storage safety monitoring. AI agents should use this when users ask "show me humidity trends for silobag 1", "has humidity been stable in silobag 2", or need historical humidity data for storage management. Get historical intergranular humidity readings for moisture migration analysis

get_quality_assessment

Returns quality score, risk level, estimated remaining storage life, and recommended actions. Essential for grain quality monitoring, marketing timing decisions, and storage duration optimization. AI agents should reference this when users ask "what is the grain quality in silobag 3", "assess storage conditions for silobag 5", or need quality assessment data for storage management and marketing decisions. Get AI-powered grain quality assessment for a specific silobag

get_rupture_alerts

Rupture alerts indicate tears, holes, or structural damage to silobags that could expose grain to weather, pests, and spoilage. Returns alert severity, location of rupture, detection timestamp, and recommended actions. Essential for silobag integrity monitoring, grain protection, and preventing quality loss. AI agents should use this when users ask "are there any silobag ruptures detected", "show rupture alerts for silobag 3", or need structural integrity alerts for silobag management. Optional silobag_id filters alerts for a specific silobag. Get silobag rupture detection alerts for all silobags or a specific one

get_satellite_data

Essential for understanding external risk factors, weather impact assessment, and proactive silobag protection. AI agents should use this when users ask "what is the satellite data for silobag 2", "show external conditions affecting silobag 4", or need environmental context for silobag management decisions. Get satellite-based monitoring data for external silobag conditions

get_sensor_health

Returns sensor IDs, positions (depth/location), battery levels, signal strength, last communication time, and operational status (active, low battery, offline). Essential for sensor network maintenance, data continuity assurance, and monitoring system reliability. AI agents should reference this when users ask "are all sensors working in silobag 5", "which sensors need battery replacement", or need sensor network health data for system administration. Get health status of IoT sensors deployed in a silobag

get_silobag_details

Essential for understanding silobag context before analyzing sensor data, planning aeration strategies, or generating quality reports. AI agents should reference this when users ask "tell me about silobag 3", "what grain is stored in silobag 5", or need detailed silobag metadata for informed analysis. Get detailed information about a specific silobag or conventional silo

get_silobags

Returns silobag IDs, names, locations, grain types, fill levels, and current monitoring status. Essential for facility overview, silobag inventory management, and selecting specific silobags for detailed analysis. AI agents should use this when users ask "show me all my silobags", "list monitored storage units", or need to identify available silobags before querying sensor readings or alerts. List all silobags and conventional silos monitored by Wiagro

get_temperature_history

Temperature increases often indicate active spoilage, insect activity, or mold growth. Returns time-series temperature data (Celsius) with timestamps from multiple sensor depths and positions. Essential for hot spot detection, spoilage heating identification, and grain quality preservation. AI agents should reference this when users ask "show me temperature trends for silobag 4", "are there any hot spots developing in silobag 6", or need historical temperature data for spoilage analysis. Optional days parameter controls lookback period. Get historical temperature readings to detect hot spots and spoilage heating in a silobag

Example Prompts for Wiagro in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Wiagro immediately.

"Show me the current temperature, humidity, and CO2 readings for silobag 3."

"Check for any silobag rupture alerts or active warnings across my facility."

"Give me a quality assessment for all my monitored silobags."

Troubleshooting Wiagro MCP Server with LangChain

Common issues when connecting Wiagro to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Wiagro + LangChain FAQ

Common questions about integrating Wiagro MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.